# Feature Enhancement Network: A Refined Scene Text Detector

arXiv: https://arxiv.org/abs/1711.04249 · HTML: https://mlsanity.com/paper/1711.04249/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection](https://mlsanity.com/paper/2109.03426/) (2021)
- [Industrial Scene Text Detection with Refined Feature-attentive Network](https://mlsanity.com/paper/2110.12663/) (2021)
- [Real-time Scene Text Detection Based on Global Level and Word Level Features](https://mlsanity.com/paper/2203.05251/) (2022)

## Abstract (arXiv)

In this paper, we propose a refined scene text detector with a textitnovel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with textitonly 3× 3 sliding-window feature and text detection refinement with textitsingle scale high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with textittask-specific, textitlow and textithigh level semantic features fusion to improve the performance of text detection. Besides, since textitunitary position-sensitive RoI pooling in general object detection is unreasonable for variable text regions, an textitadaptively weighted position-sensitive RoI pooling layer is devised for further enhancing the detecting accuracy. To tackle the textitsample-imbalance problem during the refinement stage, we also propose an effective textitpositives mining strategy for efficiently training our network. Experiments on ICDAR 2011 and 2013 robust text detection benchmarks demonstrate that our method can achieve state-of-the-art results, outperforming all reported methods in terms of F-measure.